Why does Data Visualization matter?
Data visualization refers to techniques used to communicate insights from data through visual representation. Its main goal is to distill large datasets into visual graphics to allow for easy understanding of complex relationships within the data. It is often used interchangeably with terms such as information graphics, statistical graphics, and information visualization.
It is one of the steps of the data science process developed by Joe Blitzstein, which is a framework for approaching data science tasks. After data is collected, processed, and modeled, the relationships need to be visualized so a conclusion can be made.
It’s also a component of the broader discipline of data presentation architecture (DPA), which seeks to identify, locate, manipulate, format, and present data in the most efficient way.
According to the World Economic Forum, the world produces 2.5 quintillion bytes of data every day, and 90% of all data has been created in the last two years. With so much data, it’s become increasingly difficult to manage and make sense of it all. It would be impossible for any single person to wade through data line-by-line and see distinct patterns and make observations. Data proliferation can be managed as part of the data science process, which includes data visualization.
Better Decision Making
Today more than ever, organizations are using data visualizations, and data tools, to ask better questions and make better decisions. Emerging computer technologies and new user-friendly software programs have made it easy to learn more about your company and make better data-driven business decisions.
The strong emphasis on performance metrics, data dashboards, and Key Performance Indicators (KPIs) shows the importance of measuring and monitoring company data. Common quantitative information measured by businesses includes units or product sold, revenue by quarter, department expenses, employee stats and company market share.
Data visualizations and information graphics (infographics) have become an essential tool for today’s mainstream media.
Data journalism is on the rise and journalists consistently rely on quality visualizations tools to help them tell stories about the world around us. Many well-respected institutions have fully embraced data-driven news including The New York Times, The Guardian, The Washington Post, Scientific American, CNN, Bloomberg, The Huffington Post and The Economist.
Marketers also benefit greatly from the combination of quality data and emotional storytelling. Good marketers make data-driven decisions on a daily basis, but sharing with their customers requires a different approach – one that touches both intelligently and emotionally. Data visualizations help marketers share their message using statistics and heart.
Being able to understand and read data visualizations has become a necessary requirement for the 21st century. Because data visualization tools and resources have become readily available, more and more non-technical professionals are expected to be able to gather insights from data.
Increasing data literacy around the world has been one of the main pillars of Infogram’s mission from day one.
Infogram CEO Mikko Jarvenpaa explains: “We truly believe in the importance of data education and support around the world. We believe that better-informed people make better decisions, and people who can both read and create data-driven communications are central to this.” Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends and correlations that might not otherwise be detected can be exposed.
Python offers multiple great graphing libraries that come packed with lots of different features.
No matter if you want to create interactive, live or highly customized plots python has an excellent library for you. Python is not only used to apply powerful ML algorithms but also has inherent features to tell amazing stories of data powered through its visualizations and this enables data to show insights through stories and create visualizations that enable the decision makers to understand data well and to implement changes effectively.
We will look at the main libraries in python that are used to create exciting plots and enable us to draw inferences out of them. Some of the main libraries in python are as under:
Matplotlib: low level, provides lots of freedom
Matplotlib is the backbone of Python data visualization libraries. Despite being over a decade old, it’s still the most widely used library for plotting in the Python community. It was designed to closely resemble MATLAB, a proprietary programming language developed in the 1980s. Matplotlib was the first Python data visualization library and so many other libraries are built on top of it or designed to work in tandem with it during analysis. Some libraries like pandas and Seaborn are wrappers over matplotlib. They allow you to access a number of matplotlib’s methods with less code.
Seaborn: high-level interface, great default styles
It harnesses the power of matplotlib to create beautiful charts by using only a few lines of code. The key difference is Seaborn’s default styles and color palettes, which are designed to be more aesthetically pleasing and modern. Since Seaborn is built on top of matplotlib, you will need to know matplotlib to tweak Seaborn’s defaults.
Pandas Visualization: easy to use interface, built on Matplotlib
Pandas visualization based on matplotlib API can be used to create decent plots such as bar graphs, histograms, scatter plots, etc. There are other advanced visualization libraries such as seaborn, bokeh, etc for advanced techniques such as 3D modelling, live-streaming graphs, maps, etc.
ggplot: based on R’s ggplot2, uses Grammar of Graphics
ggplot initializes a ggplot object. It can be used to declare the input data frame for a graphic and to specify the set of plot aesthetics intended to be common throughout all subsequent layers unless specifically overridden.
Plotly is an interactive, open-source, and browser-based graphing library for Python. Built on top of plotly.js, plotly is a high-level, declarative charting library. plotly.js ships with over 30 chart types, including scientific charts, 3D graphs, statistical charts, SVG maps, financial charts, and more. Plotly is MIT Licensed. Plotly graphs can be viewed in Jupyter notebooks, standalone HTML files, or hosted online on plot.ly.
In this article, we looked how effective visualization in python can be by using different varieties of libraries. It is equally important to understand which types is suited for which plots and how to combine it all effectively for better insights. Visualization of data in itself is a domain and a necessity to understand data fundamentally and communicate the results effectively. Hope this article was useful for you. Feel free to add in comments what type of python libraries you prefer to use.
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